Tracking and classifying Amazon fire events in near real time

Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near–real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies.

Considering all confidence levels, 31% of active fires attributed to forest fires occurred in fires with 10% or more overlap with 2014-2019 deforestation. However, the majority of this overlap with deforestation was confined to low (57%) and moderate (57%) confidence classes. Overall, low deforestation fractions in fires classified as forest fires suggests that most forest fires originated from other ignition sources, including abundant small clearing and agricultural fires across the region, rather than deforestation fires. However, a more detailed study of this relationship is warranted, based on the ignition time and locations for individual fire events in this study.
Evaluation of results compared to the Monitoring of the Andean Amazon Project (MAAP) To provide an independent assessment of the accuracy of our model, we compared our fire type classification to 2,099 "major" fires identified by the MAAP project for the Brazilian Amazon and available for 2020 (27). To enable this comparison, we expanded our dataset to include 2020 based on the same methodology. MAAP classified fires that produce significant smoke into four distinct classes, "deforestation fires" associated with clear-felled vegetation for agricultural expansion, "forest fires", "cropland and pasture", and "grassland" based on expert visual interpretation of various satellite data products, including high-resolution (<4 m) commercial imagery. The comparison of our individual fire classification product to this subset of Amazon fire activity provides an initial estimate of errors of omission and commission but also highlight differences in fire type definitions (Fig. S10). Close agreement between MAAP and our dataset was found, with 75% of deforestation, 69% of grassland, and 64% of forest fires identified by MAAP accurately classified in this study, giving an approximate estimate of errors of omission. However, these results also reflect differences in the definitions. For example, 23% of MAAP grassland fires were classified as forest fire by our approach, and 15% of forest fires as savanna fires, possibly the result of different thresholds to separate forests and savannas. Our results also highlight the challenges associated with accurately separating forest and deforestation fires, with 22% of MAAP deforestation fires being misclassified as forest fire in our study. A lower percentage of active fire detections classified as deforestation (57%), savanna (18%), and forest (47%) in our study had the same classification in MAAP, highlighting larger errors of commission. However, these differences were to a large extent driven by the inclusion of a specific cropland and pasture fire class in MAAP, that had strong overlap with savanna fires (50%), but also with deforestation (31%) and forest fires (29%) in our study. The significant overlap of MAAP cropland and pasture fires with our deforestation fire class could be expected for two reasons. First, MAAP uses a two year-threshold to assign deforestation fires to recent clearing, while we included 5 years of historic data (Fig. S2). Second, pasture burning often includes some residual clearing and is therefore often characterized by higher fuel consumption (Fig. S14, 29), when looking at the fire behavior this might appear similar to fires following earlier stages of deforestation. Similarly, many forest fires originate from burning in open cover types and burn into neighboring forest areas, and it is unclear how MAAP resolves these mixed fire types. In our approach, fire type is based on the fractional tree cover within the final burn perimeter, among other metrics. Overall, largely consistent results from the two approaches highlights the novelty of both products and the potential for these datasets to inform regional land management, conservation, and scientific advances regarding the nature and impacts of fire in the Amazon and surrounding biomes.

Fire type classification accuracy compared to Sentinel-2 image pairs
We assessed the accuracy at which we can separate deforestation from understory forest fires, the two main types of multi-day fires in the South American study region and an important distinction for accurate estimates of carbon emissions from deforestation and forest degradation ( Table S2). Because of the class imbalance, we took a stratified random sample of 100 deforestation and 100 forest fires across the South American study domain in 2019. We focused on fires that started in August, to avoid issues of cloud cover, and used pre-and post-fire Sentinel-2 images at 10 m resolution to interpret the reference fire type. In total, this resulted in a reference set of 194 fires across 118 image pairs after excluding 6 fires due to cloud cover (N=1) or inconclusive evidence of burning (N=5). Overall, the accuracy of fire type classification improved with fire size. The overall accuracy was 66% for fire events and 92% for active fire detections (large fires include more active fire detections; Table S2). The largest difference in accuracy was observed for forest fires, with 55% User's accuracy (reflecting commission error) for fire events increasing to 93% User's accuracy for fire detections, likely indicating a more skewed distribution of fire size and associated fire detections compared to deforestation fires. Deforestation fire classification was more stable, with 78% User's and 63% Producer's accuracy for fire events, and 87% User's and 71% Producer's accuracy for fire detections.

Algorithm accuracy in near-real time
To better understand the algorithm performance in near-real time, we compared our nearreal time results to the end of year classification. The long duration of most deforestation, forest, and savanna fires increases the accuracy of the near-real time fire type classification ( Fig. 4 and S17). Even though it takes time to accurately separate deforestation and forest fires from other fire types (e.g., Fig. S6), individual fires often burn for weeks or months. As a result, during the peak of the burning season (August and September), on average 86% of all fire detections were associated with fires that ignited on a previous day (Fig. 4), compared to only 14% of all fire detections from new fire starts. Thus, the ability to attribute fire detections to specific fire types on any given day reflects the weighted average of the classification accuracy of multi-day fires (days 2+, 86%) and the classification accuracy of new fire starts (day 1, 14%). Using the end-of-season classification as the reference, about 76% of active fire detections from deforestation fires were correctly classified on the same day during August and September, increasing to 92% with a oneweek lag time. Forest fire classification improved markedly over the season, based on the growing fraction of active fire detections from long-duration fires. Attribution of small clearing and agricultural fires was consistent throughout the 2019 fire season, since most start in this class at initial detection.    Fires were separated into deforestation, forest, small clearing and agricultural, and savanna and grassland fires using metrics of fire behavior and land cover information. The initial separation between high-confidence deforestation fires and savanna and grassland fires uses historic deforestation data (2014 -2018) and fractional tree cover (2014). For all remaining fires with ≥50% tree cover, we first isolate small clearing and agricultural fires based on low fire persistence and number of fire detections. To further separate deforestation fires from forest fires we use a separate classification for low biomass (left column, <120 t ha -1 ) and high biomass (right column, ≥120 t ha -1 ) systems based on observed differences in fire behavior in moist and dry forests, respectively (see Fig. S4).        Overall Accuracy = 92% Fig. S11: Scaling approach to convert fire perimeters from active fires to estimated burned area by fire type. We used two separate scaling factors for (a) deforestation and (b) savanna fires to match burned area within fire perimeters derived here to MODIS collection 6 burned area estimates (28). Fixed scaling factors were used for small clearing and agricultural fires and forest fires (see methods). (c) shows total estimated burned area from all fire types combined and (d) shows the relative contribution of burned area estimated here to the sum of MCD64A1 and burned area from this study. In (d), a value of 0.5 indicates equal burned area between both datasets, with observed deviation originating from burned area estimates in the small clearing and agricultural fires and forest fire classes that were not matched to MCD64A1. All subplots are shown at 0.5° resolution and grid cells with less than 1 km 2 burned area in (c) are masked white to aid interpretation.

Supplementary Data for this manuscript
Data files S1 and S2 for 2019 and 2020 are archived alongside this manuscript (https://doi.org/10.5281/zenodo.6641625) and data are provided in near real time at www.globalfiredata.org.
Data S1: Two shapefiles of individual fire events by fire type for Southern Hemisphere South America (0-25°S). The first shapefile includes data from April -December 2019 and the second for 2020.
Data S1: Explanation of shapefile attribute